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| Original file line number | Diff line number | Diff line change |
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| #!/bin/bash | ||
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| SET=$1 | ||
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| if [[ $SET != "train" && $SET != "test" && $SET != "all" && $SET != "results" ]]; then | ||
| echo "Usage: ./download_dataset.sh SET" | ||
| echo "SET options:" | ||
| echo " \t train - download training data (25 GB)" | ||
| echo " \t test - download testing data (16 GB)" | ||
| echo " \t all - download both training and testing data (41 GB)" | ||
| echo " \t results - download results of Bonneel et al. and our aproach ( GB)" | ||
| exit 1 | ||
| fi | ||
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| URL=http://vllab1.ucmerced.edu/~wlai24/video_consistency/data | ||
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| if [[ $SET == "train" ]]; then | ||
| wget -N $URL/train.zip | ||
| unzip train.zip | ||
| fi | ||
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| if [[ $SET == "test" ]]; then | ||
| wget -N $URL/test.zip | ||
| unzip test.zip | ||
| fi | ||
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| if [[ $SET == "all" ]]; then | ||
| wget -N $URL/train.zip | ||
| unzip train.zip | ||
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| wget -N $URL/test.zip | ||
| unzip test.zip | ||
| fi | ||
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| if [[ $SET == "results" ]]; then | ||
| wget -N $URL/results.zip | ||
| unzip results.zip | ||
| fi |
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| Original file line number | Diff line number | Diff line change |
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| @@ -0,0 +1,168 @@ | ||
| #!/usr/bin/python | ||
| from __future__ import print_function | ||
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| ### python lib | ||
| import os, sys, argparse, glob, re, math, pickle, cv2, time | ||
| import numpy as np | ||
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| ### torch lib | ||
| import torch | ||
| import torch.nn as nn | ||
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| ### custom lib | ||
| from networks.resample2d_package.modules.resample2d import Resample2d | ||
| import networks | ||
| import utils | ||
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| if __name__ == "__main__": | ||
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| parser = argparse.ArgumentParser(description='Fast Blind Video Temporal Consistency') | ||
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| ### dataset options | ||
| parser.add_argument('-dataset', type=str, required=True, help='dataset to test') | ||
| parser.add_argument('-phase', type=str, default="test", choices=["train", "test"]) | ||
| parser.add_argument('-data_dir', type=str, default='data', help='path to data folder') | ||
| parser.add_argument('-list_dir', type=str, default='lists', help='path to list folder') | ||
| parser.add_argument('-task', type=str, required=True, help='evaluated task') | ||
| parser.add_argument('-redo', action="store_true", help='Re-generate results') | ||
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| ### other options | ||
| parser.add_argument('-gpu', type=int, default=0, help='gpu device id') | ||
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| opts = parser.parse_args() | ||
| opts.cuda = True | ||
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| opts.size_multiplier = 2 ** 2 ## Inputs to TransformNet need to be divided by 4 | ||
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| print(opts) | ||
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| if opts.cuda and not torch.cuda.is_available(): | ||
| raise Exception("No GPU found, please run without -cuda") | ||
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| ### load model opts | ||
| opts_filename = os.path.join('pretrained_models', "ECCV18_blind_consistency_opts.pth") | ||
| print("Load %s" %opts_filename) | ||
| with open(opts_filename, 'r') as f: | ||
| model_opts = pickle.load(f) | ||
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| ### initialize model | ||
| print('===> Initializing model from %s...' %model_opts.model) | ||
| model = networks.__dict__[model_opts.model](model_opts, nc_in=12, nc_out=3) | ||
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| ### load trained model | ||
| model_filename = os.path.join('pretrained_models', "ECCV18_blind_consistency.pth") | ||
| print("Load %s" %model_filename) | ||
| state_dict = torch.load(model_filename) | ||
| model.load_state_dict(state_dict['model']) | ||
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| ### convert to GPU | ||
| device = torch.device("cuda" if opts.cuda else "cpu") | ||
| model = model.to(device) | ||
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| model.eval() | ||
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| ### load video list | ||
| list_filename = os.path.join(opts.list_dir, "%s_%s.txt" %(opts.dataset, opts.phase)) | ||
| with open(list_filename) as f: | ||
| video_list = [line.rstrip() for line in f.readlines()] | ||
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| times = [] | ||
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| ### start testing | ||
| for v in range(len(video_list)): | ||
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| video = video_list[v] | ||
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| print("Test %s on %s-%s video %d/%d: %s" %(opts.task, opts.dataset, opts.phase, v + 1, len(video_list), video)) | ||
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| ## setup path | ||
| input_dir = os.path.join(opts.data_dir, opts.phase, "input", opts.dataset, video) | ||
| process_dir = os.path.join(opts.data_dir, opts.phase, "processed", opts.task, opts.dataset, video) | ||
| output_dir = os.path.join(opts.data_dir, opts.phase, "ECCV18", opts.task, opts.dataset, video) | ||
| if not os.path.isdir(output_dir): | ||
| os.makedirs(output_dir) | ||
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| frame_list = glob.glob(os.path.join(input_dir, "*.jpg")) | ||
| output_list = glob.glob(os.path.join(output_dir, "*.jpg")) | ||
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| if len(frame_list) == len(output_list) and not opts.redo: | ||
| print("Output frames exist, skip...") | ||
| continue | ||
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| ## frame 0 | ||
| frame_p1 = utils.read_img(os.path.join(process_dir, "00000.jpg")) | ||
| output_filename = os.path.join(output_dir, "00000.jpg") | ||
| utils.save_img(frame_p1, output_filename) | ||
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| lstm_state = None | ||
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| for t in range(1, len(frame_list)): | ||
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| ### load frames | ||
| frame_i1 = utils.read_img(os.path.join(input_dir, "%05d.jpg" %(t - 1))) | ||
| frame_i2 = utils.read_img(os.path.join(input_dir, "%05d.jpg" %(t))) | ||
| frame_o1 = utils.read_img(os.path.join(output_dir, "%05d.jpg" %(t - 1))) | ||
| frame_p2 = utils.read_img(os.path.join(process_dir, "%05d.jpg" %(t))) | ||
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| ### resize image | ||
| H_orig = frame_p2.shape[0] | ||
| W_orig = frame_p2.shape[1] | ||
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| H_sc = int(math.ceil(float(H_orig) / opts.size_multiplier) * opts.size_multiplier) | ||
| W_sc = int(math.ceil(float(W_orig) / opts.size_multiplier) * opts.size_multiplier) | ||
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| frame_i1 = cv2.resize(frame_i1, (W_sc, H_sc)) | ||
| frame_i2 = cv2.resize(frame_i2, (W_sc, H_sc)) | ||
| frame_o1 = cv2.resize(frame_o1, (W_sc, H_sc)) | ||
| frame_p2 = cv2.resize(frame_p2, (W_sc, H_sc)) | ||
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| with torch.no_grad(): | ||
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| ### convert to tensor | ||
| frame_i1 = utils.img2tensor(frame_i1).to(device) | ||
| frame_i2 = utils.img2tensor(frame_i2).to(device) | ||
| frame_o1 = utils.img2tensor(frame_o1).to(device) | ||
| frame_p2 = utils.img2tensor(frame_p2).to(device) | ||
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| ### model input | ||
| inputs = torch.cat((frame_p2, frame_o1, frame_i2, frame_o1), dim=1) | ||
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| ### forward | ||
| ts = time.time() | ||
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| output, lstm_state = model(inputs, lstm_state) | ||
| frame_o2 = frame_p2 + output | ||
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| te = time.time() | ||
| times.append(te - ts) | ||
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| ## create new variable to detach from graph and avoid memory accumulation | ||
| lstm_state = utils.repackage_hidden(lstm_state) | ||
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| ### convert to numpy array | ||
| frame_o2 = utils.tensor2img(frame_o2) | ||
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| ### resize to original size | ||
| frame_o2 = cv2.resize(frame_o2, (W_orig, H_orig)) | ||
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| ### save output frame | ||
| output_filename = os.path.join(output_dir, "%05d.jpg" %(t)) | ||
| utils.save_img(frame_o2, output_filename) | ||
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| ## end of frame | ||
| ## end of video | ||
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| if len(times) > 0: | ||
| time_avg = sum(times) / len(times) | ||
| print("Average time = %f seconds (Total %d frames)" %(time_avg, len(times))) |